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CLASSICAL INDIAN SKEPTICISM: REFORMING OR REJECTING PHILOSOPHY

2019· article· en· W2997827732 on OpenAlexaff
Jennifer Nagel

Bibliographic record

VenueComparative Philosophy An International Journal of Constructive Engagement of Distinct Approaches toward World Philosophy · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSkepticismPhilosophyEpistemology

Abstract

fetched live from OpenAlex

One of the most striking features of Classical Indian skepticism is the degree to which it provides intellectual delight. Ethan Mills offers an insightful treatment of each of the three pivotal figures he locates in this tradition, but he also succeeds in conveying that sense of delight, both in his sympathetic depictions of the tradition's great skeptical arguments, and in his own creative interpretations of their significance. There are many points where I find Mills fully persuasive: for example, in his arguments to the effect that we cannot know the precise boundaries of our ignorance, and in his suggestion that skepticism can arise inevitably from an exaggerated emphasis on explicit justification. As a critic, however, I sense a duty to explore the more puzzling aspects of Mills' approach, with the aim of clearing away some ambiguities and gaining a deeper appreciation of what it is that he is proposing. I should acknowledge that I come at the book from an odd angle, as a contemporary epistemologist with no more than an amateur's grasp of the South Asian texts he is discussing. But in saying I read these texts as a contemporary epistemologist, I don't mean to say that I aim to impose my contemporary epistemology upon them, as opposed to letting it be shaped and instructed by them. I certainly agree with Mills that we should not assume that the moves made by Classical Indian skeptics can be neatly captured with the existing vocabulary of contemporary Anglo-American epistemology; indeed, much of the reward for me in looking at these traditions lies in their potential to offer fresh philosophical paths, and correctives to bad habits I have picked up from the traditions in which I was educated, and Mills' book has been eyeopening in that regard.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.246
GPT teacher head0.324
Teacher spread0.078 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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